To process the IMU data, most methods exploit highly hand-crafted models to approximate sensor characteristics and the underlying motion dynamics.
A representative pipeline can be summarized by [1]}: data filtering (e.g., de-noising), intrinsic model compensation, data interpolation and extrapolation, pose integration, probabilistic sensor fusion or update.
The first two steps are generally recognized as data pre-processing, whose objective is to obtain a `cleaned' version of sensory input by reducing errors caused by mechanical, electronic, and signal processing imperfections. The next two steps are to calculate IMU's integrated orientation and position based on the underlying motion dynamics. To reduce the inevitable drift from the IMU integration, probabilistic update using data from other sensors [2]}, [3]} or motion constraints [4]}, [5]} is also implemented in a variety of applications.
